An Adaptive Opposition Learning-Improved Slime Mould Algorithm-Based Optimization Routing for Guaranteeing Reliable Data Dissemination in FANETs

Abstract

Flying Ad hoc NETwork (FANET) refers to a self-organizing wireless network that facilitates easy, flexible and inexpensive deployment of flying nodes termed as Unmanned Aerial Vehicles (UAVs). These UAVs communicate with one another without the presence of any fixed network infrastructure. The routing process is responsible for achieving reliable coordination and cooperation among flying nodes to establish reliable routes towards radio access infrastructure that corresponds to Base Station (BS) of FANET. Routing protocols in FANETs play an anchor role in preventing network partitions and link disconnections to guarantee prolonged route lifetime with minimized energy utilization rate. In this paper, an Adaptive Opposition Learning-Improved Slime Mould Algorithm (AOLISMA)-based optimization routing is proposed for ensuring reliable data dissemination among UAVs with extended network lifetime and minimized energy consumption. This AOLISMA routing approach utilizes two randomly selected search agents for determining feasible direction and displacement that aid in better routing process. It adopts random selection of search agents for restricting the limits of exploration and exploitation to establish better balance during the process of routing. It also helps in attaining a near-optimal or optimal route that prolongs network route lifetime. It specifically utilizes opposition learning for adaptive increase in the exploration rate for identifying feasible routes from which, optimal route can be selected based on an objective function for attaining reliable routing. The simulation experiments of the proposed AOLISMA scheme conducted based on throughput, control overhead, mean delay and energy consumption for varying mobility rates of UAVs confirm better performance on par with the baseline GAR, ACOAR and ABCAR approaches taken for comparison.